Video Coding

ABSTRACT

A method of encoding an outgoing video stream comprising a plurality of frames, each frame comprising a plurality of image portions, the method including for each target image portion to be encoded, selecting a preferred one of a set of encoding modes by optimizing a function comprising an estimate of distortion and a measure of bit rate required to encode the target image portion; encoding the target image portion into the outgoing video stream using the selected encoding mode; and transmitting the encoded outgoing video stream over a lossy channel. The estimate of distortion comprises a first term representing source coding distortion, and a bias term representing an estimate of distortion that would be experienced due to loss over said channel. The bias term is determined based on a trained parameter trained based on a sample video stream.

RELATED APPLICATION

This application claims priority under 35 U.S.C. §119 or 365 to Great Britain Application No. GB 1110763.8, filed Jun. 24, 2011. The entire teachings of the above application are incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to balancing a trade-off between bitrate and distortion when encoding a video signal using intra and inter frame encoding.

BACKGROUND

A stream of video data to be encoded is illustrated schematically in FIG. 1 a. The stream comprises multiple frames (F) each representing the video image at a different respective moment in time. As will be familiar to a person skilled in the art, for the purpose of encoding, each frame (F) is divided into portions and each portion may also be subdivided into smaller sub-portions, each portion or sub-portion comprising a plurality of pixels. For example, according to one terminology each frame of a video stream to be encoded is divided into macroblocks (MB) and each macroblock is sub-divided into blocks (b), each block comprising multiple pixels. Each frame may also be divided into independently decodable slices (S), each slice comprising one or more macroblocks. N.B. the divisions shown in FIG. 1 a are only schematic for illustrative purposes and it will be appreciated that these are not necessarily meant to correspond to any actual encoding scheme—e.g. each frame is likely to contain a larger number of macroblocks.

A goal of a video codec is to reduce the bit rate needed to transmit a video signal, while maintaining highest possible quality. This goal is achieved by exploiting statistical redundancies (similarities in the video signal) and perceptual irrelevancies (related to sensitivity of human visual system).

Most of today's video codecs are based on an architecture that includes prediction of pixel blocks from other pixel blocks, transform of prediction residuals, quantization of transform coefficients, and entropy coding of quantization indices. These steps contribute to reducing redundancies and irrelevancies.

The prediction typically can be done from pixels in video frames different from the current frame (inter prediction) and from pixels in the same frame (intra prediction). That is, if encoded using intra frame encoding then a block or portion of the frame (the target block or portion) is encoded relative to another block or image portion in the frame (the reference block or portion); and if encoded using inter frame encoding then the target block or portion is encoded relative to a reference block or portion in another frame. This process is commonly referred to as prediction or prediction coding. The inter or intra prediction module will thus generate a prediction e.g. in the form of an indication of a neighboring block in the case of intra frame encoding and/or a motion vector in the case of inter frame encoding. Typically the encoder also generates a residual signal representing a “left over” difference between the predicted block and the actual block. The residual, motion vectors and any required data associated with the intra prediction are then output into the encoded video stream, typically via further coding stages such as a quantizer and entropy encoder. Hence most blocks in the video can be encoded in terms of a difference between blocks, which require fewer bits to encode than encoding absolute pixel values and hence saves on bitrate. Intra prediction encoding typically requires more bits than inter prediction, though still represents a saving over encoding absolute values. Details of suitable inter and intra encoding techniques for video will be familiar to a person skilled in the art.

Modern codecs allow the use of different prediction encoding modes for different portions within a frame. The possibility of having different coding options increases the rate-distortion efficiency of a video codec. The optimal coding representation has to be found for every frame region. Typically, such region is a macroblock, e.g. of 16×16 pixels. I.e. so it is possible for an intra prediction or inter prediction mode to be selected individually for each macroblock, so that different macroblocks within the same frame can be encoded with different modes. It is also possible in some codecs to use different modes based on different levels of partitioning of macroblocks, e.g. selecting between a higher complexity mode in which a separate prediction is performed for each 4×4 block within a macroblock or a lower complexity mode in which prediction is performed based on only 8×8 or 8×16 blocks or even whole macroblocks. The available modes may also include different options for performing prediction. For example as illustrated schematically in FIG. 1 b, in one intra mode the pixels of a 4×4 block (b) may be determined by extrapolating down from the neighboring pixels from the block immediately above, or by extrapolating sideways from the block immediately to the left. Another special prediction mode called “skip mode” may also be provided in some codecs, which may be considered as an alternative type of inter mode. In skip mode (PSkip) the target's motion vector is inferred based on the motion vectors to the top and to the left and there is no encoding of residual coefficients. The manner in which the motion vector is inferred is consistent with motion vector prediction, and thus the motion vector difference is zero so it is only required to signal that the MB is a skip block.

According to the above, a coding representation may thus include block partition information, prediction mode, motion vector, quantization accuracy, etc. The optimal coding option depends on video content, bit rate, earlier coding decisions, etc. The accuracy of quantization of transform coefficients is typically chosen to meet a bit rate constraint. Furthermore, distortion should be minimized.

For example, the H.264 video coder provides a great flexibility in choosing the prediction mode. For inter prediction of the luma component, a macroblock of 16×16 pixels can be represented as one block of 16×16 pixels, or two blocks of 16×8 pixels, or two blocks of 8×16 pixels, or four blocks of 8×8 pixels. Further, an 8×8 block can be represented as one block of 8×8 pixels, or two blocks of 8×4 pixels, or two blocks 4×8 pixels, or four blocks of 4×4 pixels. The inter prediction is tried for each allowed partition of a macroblock. The inter prediction of a block is represented by indexing the reference frame(s) and the motion vector(s) (spatial shift from the reference block in the respective reference frame), which typically are estimated with sub-pixel precision. For intra prediction of the luma component, there are four possible modes for 16×16 blocks and nine possible modes for 4×4 blocks. Further, there are four possible modes for chroma components. The best prediction mode is chosen by comparing the performance of inter and intra prediction modes.

The rate-distortion performance of a video codec such as H.264 AVC depends to a large extent on the performance of the macroblock mode selection o. That is, the procedure of determining whether the macroblock is best encoded, in terms of rate-distortion trade-offs, using e.g. intra mode or inter mode (predicted from previously encoded frame). From a robustness perspective, intra coded macroblocks are beneficial since they stop temporal error propagation (assuming the use of constrained intra prediction, i.e. intra prediction from inter predicted macroblocks is prohibited). However, intra coded macroblocks are generally more expensive in terms of rate compared to inter coded macroblocks, and thus it is important to introduce intra coded macroblocks systematically such that the distortion (e.g. average distortion) at the decoder is minimized given a certain bit budget.

The rate-distortion performance optimization problem can be formulated in terms of minimizing distortion under a bit rate constraint R. A Lagrangian optimization framework is often used to solve the problem. There, the optimization criterion is formulated as

J=D(m,o)+λR(m,o),  (1)

where J represents the Lagrange function, D represents a measure of distortion (a function of mode o and macroblock m or macroblock sub-partition), R is the bitrate, and λ is a parameter defining a trade-off between distortion and rate.

In this application solving the Lagrangian optimization problem means finding the encoding mode o which minimizes the Lagrange function J, where the Lagrange function J comprises at least a term representing distortion, a term representing bitrate, and a factor (the “Lagrange multiplier”) representing a tradeoff between the two. As the encoding mode o is varied towards more thorough or better quality encoding modes then the distortion term D will decrease. However, at the same time the rate term R will increase, and at a certain point dependent on λ the increase in R will outweigh the decrease in D. Hence the expression J will have some minimum value, and the encoding mode o at which this occurs is considered the optimal encoding mode.

In this sense the bitrate R, or rather the term λR, places a constraint on the optimization in that this term pulls the optimal encoding mode back from ever increasing quality. The mode at which this optimal balance is found will depend on λ, and hence λ may be considered to represent a tradeoff between bitrate and distortion.

The Lagrangian optimization is commonly used in the process of choosing coding decisions, and is applied for every frame region (e.g. every macroblock of 16×16 pixels).

The distortion D may be quantified as sum of squared differences (SSD) between original and reconstructed pixels; and may be evaluated to account for all processing stages including: prediction, transform (from a spatial domain representation of the pixels of each block or macroblock to a transform domain representation such as an optical frequency domain representation), and quantization (the process of converting a digital approximation of a continuous signal to more discrete, lower granularity quantization levels). Furthermore, in order to compute reconstructed pixels, steps of inverse quantization, inverse transform, and inverse prediction must be performed. SSD is often preferred as distortion criterion since it results in higher quality. Commonly, the rate term R also accounts for coding of all needed parameters, including parameters describing prediction and quantized transform coefficients. Parameters are typically coded with an entropy coder, and in that case the rate can be an estimate of the rate that would be obtained by the entropy coder, or can be obtained by actually running the entropy coder and measuring the resulting rate for each of the candidate modes. Entropy coding/decoding is a lossless process and as such doesn't affect the distortion.

This kind of process may be referred to herein as a full complexity rate-distortion optimization (or full RDO).

Zhang et al., “Error resilience video coding in H.264 encoder with potential distortion tracking”, (Proc. IEEE International Conference on Image Processing, pp. 163-166, 2004) (incorporated herein by reference in its entirety) propose a systematic framework to introduce intra coded macroblocks based on the minimization of the expected average sum of squared differences (SSD) at the decoder. Furthermore, Zhang takes into account an estimate of end-to-end distortion based on an assumption of an erroneous transmission channel. By tracking the potential distortion Zhang et al. are able to compute a bias term related to the expected error-propagation distortion (at the decoder) that is added to the source coding distortion when computing the cost for inter macroblocks within the encoder rate-distortion loop.

In Zhang et al., the authors estimate the potential distortion in the decoder due to source coding and channel errors. The estimated potential distortion is then indirectly used to bias the mode selection towards intra coding (if there is a probability of channel errors).

Zhang's so-called end-to-end distortion expression is based on the sum of squared differences (SSD) distortion measure and assumes a Bernoulli distribution for losing macroblocks. The optimal macroblock mode o_(opt) is given by:

$\begin{matrix} {{o_{opt} = {\underset{o}{argmin}\left( {{D_{s}\left( {m,o} \right)} + {D_{{ep} - {ref}}\left( {m,o} \right)} + {\lambda \; {R\left( {m,o} \right)}}} \right)}},} & (2) \end{matrix}$

where D (m,o) denotes the SSD distortion between the original and reconstructed pixel block for macroblock m and macroblock mode o, R the total rate, and A, the Lagrange multiplier relating the distortion and the rate term. D_(ep-ref)(m,o) denotes the expected distortion within the reference block in the decoder due to error propagation. D_(ep-ref)(m,o) thus provides a bias term which biases the optimization toward intra coding if error propagation distortion becomes too large. D_(ep-ref)(m,o) is zero for the intra coded macroblock modes. The expression D_(s)(m,o)+D_(ep-ref)(m,o)+λR(m,o) may be considered an instance of a Lagrange function J. Argmin_(o) outputs the value of the argument o for which the value of the expression J is minimum.

The total expected error propagation distortion map D_(ep) is driven by the performance of the error concealment and is updated after each macroblock mode selection as:

D _(ep)(m(k),n+1)=(1−p)D _(ep-ref)(m(k),n,o _(opt))+p(D _(ec-rec)(m(k),n,o _(opt))+D _(ec-ep)(m(k),n)),  (3)

where n is the frame number, m(k) denotes the k^(th) sub-partition (i.e. block) of macroblock m, p the probability of packet loss. In Zhang et al. [2] the error-propagation distortion is stored on 4×4 pixel block granularity. The error-propagation reference distortion D_(ep-ref)(m,o) for a block is estimated by averaging the distortions in the error-propagation distortion map of the previous frame corresponding to the block position indicated by the motion vectors of the current block. D_(ec-rec) denotes the SSD between the reconstructed and error concealed pixels in the encoder, and D_(ec-ep) the expected SSD between the error concealed pixels in the encoder and decoder. Typically, a lost block is reconstructed by copying a block from a previous frame (e.g., using frame copy or motion copy error concealment method). In this case, D_(ec-ep) is obtained by extracting the corresponding distortion from the error propagation distortion map of the frame used for error concealment.

However, it will be seen from everything discussed above that the number of coding options can be quite high, and therefore the computational load needed to evaluate them can become a limiting factor. Given a high number of possible coding options, evaluating the Lagrangian optimization criterion that accounts for all processing stages (and also requires all inverse processing stages to be performed) can become a computationally very demanding task. Therefore, an alternative lower-complexity optimization criterion is also in common use:

J′=D′(m,o)+λ′R′(m,o)  (1a)

where D′ is the prediction distortion, and R′ is the rate for parameters describing prediction (e.g., prediction modes, motion vectors).

The prediction distortion D′ takes into account only the distortion after the intra or inter prediction (or more precisely the residual after prediction), and not the effect of other encoder stages such as transform from the spatial domain to a transform domain and quantization (or their inverses). Hence D′ represents the difference between the original and predicted samples (intra or inter), rather than the difference between the original and fully reconstructed samples. Further, this simplified distortion measure is quantified as the sum of absolute differences (SAD) between the original and predicted samples (intra or inter), which requires fewer computations compared to SSD. That is:

$\begin{matrix} {D^{\prime} = {\sum\limits_{i}\; {{s_{i} - s_{{pred}_{i}}^{\prime}}}}} & \left( {1b} \right) \end{matrix}$

where s_(i) are the original input samples and s_(pred)′_(i) are the predicted samples without taking into account the effect of being reconstructed through a forward and inverse transform and quantization. So as well as being based on SAD instead of SSD, the lower complexity distortion term D′ represents the difference between the original and predicted samples, rather than the difference between original and reconstructed pixels as represented in the full complexity version of the calculation above. Further, the rate term R′ only represents the bitrate cost of side information (motion vector or indication of intra prediction, prediction mode, and indication of macroblock partitioning); and does not take into account the bitrate cost of the transformed quantized residual.

Thus, the simplified computation only needs the prediction step to be performed. The steps of transform and quantization, as well as inverse quantization, inverse transform, and inverse prediction are omitted. Hence the complexity of evaluating performance of a coding option is therefore reduced. This kind of process may be referred to herein as low-complexity rate-distortion optimization (or low-complexity RDO).

On the other hand, since low-complexity RDO approximates the prediction step only, the resulting final rate-distortion performance is typically reduced. In the inventors' experiments they have observed that the low-complexity RDO results in performance drop of 0.5-1.5 dB compared to the full RDO at the same bit rate. Subjectively, the reconstructed videos also have lower quality.

SUMMARY

The present invention seeks to achieve a higher performance rate-distortion optimization than would result from the lowest complexity, non loss-adaptive RDO process; but without incurring the full processing cost of a full complexity loss-adaptive RDO. One way to do this would be to use a “hybrid” RDO process which used a simplified measure of source coding distortion in conjunction with a higher complexity loss-adaptive bias term. However, a good performance would not necessarily be achieved by simply combining terms based on two different types of distortion measure, e.g. SAD-based measure of the prediction error for the source coding distortion and an SSD-based distortion measure for the loss-adaptive bias term.

Hence an aim of the present invention is to try to maximize the performance of a loss-adaptive RDO process when used in the context of a simplified rate-distortion expression.

According to one aspect of the present invention, there is provided a method of encoding an outgoing video stream comprising a plurality of frames, each frame comprising a plurality of image portions, the method comprising: for each target image portion to be encoded, selecting a preferred one of a set of encoding modes by optimizing a function comprising an estimate of distortion and a measure of bit rate required to encode the target image portion; encoding the target image portion into the outgoing video stream using the selected encoding mode; and transmitting the encoded outgoing video stream over a lossy channel; wherein the estimate of distortion comprises a first term representing source coding distortion, and a bias term representing an estimate of distortion that would be experienced due to loss over said channel; and wherein the bias term is determined based on a trained parameter trained based on a sample video stream.

Hence the present invention provides a method of training a rate-distortion optimization process, which can advantageously be used to support a “hybrid” RDO process approaching the performance benefits of a full complexity loss-adaptive RDO but with reduced computational complexity.

In embodiments the bias term may be based on a second term representing an estimate of the distortion that would be experienced, if the target portion does arrive over the channel, due to non arrival of a reference portion in the target portion's history from which prediction of the target portion depends, and on a concealment term representing an estimate of distortion that would be experienced due to concealment; the concealment term may comprise a third term representing a measure of concealment distortion of the target portion relative to an image portion that would be used to conceal loss of the target portion if the target portion is lost over the channel, and a fourth term representing an estimate of distortion that would be experienced due to loss of an image portion in the target portion's history upon which concealment of the target portion depends; and one of the third term and the second term may comprise said trained parameter.

In one embodiment the third term may comprise said trained parameter.

The first term may be based on a lower complexity measure of difference between samples than one or more terms upon which the bias term is based.

The first term may be based on a sum of absolute differences between original samples and predicted samples of the target image portion.

The third term may be based on a sum of absolute differences between reconstructed samples of the target image portion and reconstructed samples of the image portion that would be used to conceal loss of the target portion.

The third term may comprise the sum of absolute differences raised to the power of the trained parameter.

The third term may comprise the sum of squared differences multiplied by the trained parameter.

The trained parameter may be trained to maximize a ratio of signal power to noise.

The trained parameter may be a function of one or more of a loss probability, coding rate and round trip time.

One or both of the second and fourth terms may be based on a sum of squared differences between samples.

The method may comprise determining a probability p that a packet will be lost over the channel, wherein the second term may be weighted by a factor of 1−p, and the concealment term may be weighted by a factor of p.

The method may comprise determining a probability p that a packet will be lost over the channel, wherein the second term may be weighted by a trained factor α(p,R) being a function of p and the bit rate R, and the concealment term may be weighted by a trained factor β(p,R) also being a function of p and the bit rate R.

The bias term may be based on an entry in an error propagation distortion map comprising said second term and concealment term, and the method may comprise: updating the error propagation distortion map after each encoding mode selection, and determining the error propagation bias term from the error propagation distortion map for use in each respective subsequent encoding mode selection.

The selected encoding mode o_(opt) may be calculated by:

$o_{opt} = {\underset{o}{argmin}\left( {{{D^{\prime}\left( {m,o} \right)} + {D_{{ep} - {ref}}\left( {m,o} \right)} + {\lambda \; {R^{\prime}\left( {m,o} \right)}}},} \right.}$

where D′(m,o)+D_(ep-ref)(m,o)+λR′(m,o) is said function, D′ is the first term, D_(ep-ref)(m,o) is the bias term, R′(m,o) is the bitrate, λ is a factor representing a trade-off between distortion and bitrate, and m is an index of the target image portion.

The method may comprise determining a probability p that a packet will be lost over the channel, wherein the updated distortion map D_(ep) for a frame n+1 may be calculated according to:

D _(ep)(m(k),n+1)=(1−p)D _(ep-ref)(m(k),n,o _(opt))+p(f(ŝ _(n)(o _(opt)),ŝ _(n−1))+D _(ec-ep)(m(k),n))

where n represents a previously encoded frame, m(k) is the k^(th) partition of the m^(th) image portion, D_(ep-ref) is the second term, f(ŝ_(n),ŝ_(n−1)) is the third term, and D_(ec-ep) is the fourth.

The third term may be calculated according to:

f(ŝ _(n) ,ŝ _(n−1))=(SAD(ŝ _(n) ,ŝ _(n−1)))^(γ)

where SAD is the sum of absolute differences and γ is the trained parameter.

The third term may be calculated according to:

f(ŝ _(n) ,ŝ _(n−1))=γ·SSD(ŝ _(n) ,ŝ _(n−1))

where SSD is the sum of squared differences and γ(p,R) is the trained parameter.

The encoding modes may comprise at least (i) an intra frame mode which encodes the target image portion relative to a reference image portion in the same frame, and (ii) an inter frame encoding mode which encodes the target image portion relative to a reference image portion in a previously encoded frame.

The set of encoding modes may comprise a plurality of intra frame modes.

The set of encoding modes may comprise a skip mode.

The first term may take into account distortion due to prediction coding but not distortion due to quantization.

The third term may take into account distortion due to prediction coding but not distortion due to quantization.

One or both of the second and fourth terms may take into account distortion due to both prediction coding and quantization.

The first term may take into account distortion due to prediction coding but not distortion due to transformation from a spatial domain representation to a transform domain representation.

The third term may take into account distortion due to prediction coding but not distortion due to quantization.

One or both of the second and fourth terms may take into account distortion due to both prediction coding and transformation from a spatial domain representation to a transform domain representation.

The bit rate may not take into account a cost of encoding a residual signal.

The bias term may integrate an effect of past loss forward over time.

In yet another embodiment, the second term may comprise said trained parameter, the method may comprise determining a probability p that a packet will be lost over the channel, and the updated distortion map D_(ep) for a frame n+1 may be calculated according to:

D _(ep)(m(k),n+1)=(1−p)f(D _(ep-ref)(m(k),n,o _(opt)))+p(D _(ec-rec)(m(k),n,o _(opt))+D _(ec-ep)(m(k),n))

where f(D_(ep-ref))=_(ep-ref) ^(γ) or γD_(ep-ref) and γ is the trained parameter.

According to another aspect of the present invention, there is provided a computer program product for encoding an outgoing video stream comprising a plurality of frames, each frame comprising a plurality of image portions, the computer program product being embodied on a non-transitory computer-readable medium and configured so as when executed on a processor to perform the operations of: for each target image portion to be encoded, selecting a preferred one of a set of encoding modes by optimizing a function comprising an estimate of distortion and a measure of bit rate required to encode the target image portion; encoding the target image portion into the outgoing video stream using the selected encoding mode; and transmitting the encoded outgoing video stream over a lossy channel; wherein the estimate of distortion comprises a first term representing source coding distortion, and a bias term representing an estimate of distortion that would be experienced due to loss over said channel; and wherein the bias term is determined based on a trained parameter trained based on a sample video stream.

In embodiments the computer program product may be further configured so as when executed to perform operations in accordance with any of the above method features.

According to another aspect of the present invention there is provided an apparatus for encoding an outgoing video stream comprising a plurality of frames, each frame comprising a plurality of image portions, the apparatus comprising: an encoder configured, for each target image portion to be encoded, to select a preferred one of a set of encoding modes by optimizing a function comprising an estimate of distortion and a measure of bit rate required to encode the target image portion; wherein the encoder is configured to encode the target image portion into the outgoing video stream using the selected encoding mode; the apparatus comprises a transmitter for transmitting the encoded outgoing video stream over a lossy channel; the estimate of distortion comprises a first term representing source coding distortion, and a bias term representing an estimate of distortion that would be experienced due to loss over said channel; and the encoder is configured such that the bias term is determined based on a trained parameter trained based on a sample video stream.

In embodiments the encoder may be further configured to perform operations in accordance with any of the above method features.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the present invention and to show how it may be put into effect, reference is made by way of example to the accompanying drawings in which:

FIG. 1 a is a schematic representation of a video stream,

FIG. 1 b is a schematic representation of some intra prediction coding modes,

FIG. 1 c is a schematic representation of an inter prediction coding mode,

FIG. 2 is a schematic block diagram of a communication system, and

FIG. 3 is a schematic block diagram of an encoder.

DETAILED DESCRIPTION

An example communication system in which video coding may be employed is illustrated schematically in the block diagram of FIG. 2. The communication system comprises a first, transmitting terminal 12 and a second, receiving terminal 22. For example, each terminal 12, 22 may comprise a mobile phone or smart phone, tablet, laptop computer, desktop computer, or other household appliance such as a television set, set-top box, stereo system, etc. The first and second terminals 12, 22 are each operatively coupled to a communication network 32 and the first, transmitting terminal 12 is thereby arranged to transmit signals which will be received by the second, receiving terminal 22. Of course the transmitting terminal 12 may also be capable of receiving signals from the receiving terminal 22 and vice versa, but for the purpose of discussion the transmission is described herein from the perspective of the first terminal 12 and the reception is described from the perspective of the second terminal 22. The communication network 32 may comprise for example a packet-based network such as a wide area interne and/or local area network, and/or a mobile cellular network.

The first terminal 12 comprises a storage medium 14 such as a flash memory or other electronic memory, a magnetic storage device, and/or an optical storage device. The first terminal 12 also comprises a processing apparatus 16 in the form of a CPU having one or more cores; a transceiver such as a wired or wireless modem having at least a transmitter 18; and a video camera 15 which may or may not be housed within the same casing as the rest of the terminal 12. The storage medium 14, video camera 15 and transmitter 18 are each operatively coupled to the processing apparatus 16, and the transmitter 18 is operatively coupled to the network 32 via a wired or wireless link. Similarly, the second terminal 22 comprises a storage medium 24 such as an electronic, magnetic, and/or an optical storage device; and a processing apparatus 26 in the form of a CPU having one or more cores. The second terminal comprises a transceiver such as a wired or wireless modem having at least a receiver 28; and a screen 25 which may or may not be housed within the same casing as the rest of the terminal 22. The storage medium 24, screen 25 and receiver 28 of the second terminal are each operatively coupled to the respective processing apparatus 26, and the receiver 28 is operatively coupled to the network 32 via a wired or wireless link.

The storage medium 14 on the first terminal 12 stores at least a video encoder arranged to be executed on the processing apparatus 16. When executed the encoder receives a “raw” (unencoded) input video stream from the video camera 15, encodes the video stream so as to compress it into a lower bitrate stream, and outputs the encoded video stream for transmission via the transmitter 18 and communication network 32 to the receiver 28 of the second terminal 22. The storage medium on the second terminal 22 stores at least a video decoder arranged to be executed on its own processing apparatus 26. When executed the decoder receives the encoded video stream from the receiver 28 and decodes it for output to the screen 25. A generic term that may be used to refer to an encoder and/or decoder is a codec.

FIG. 3 is a high-level block diagram schematically illustrating an encoder such as might be implemented on transmitting terminal 12. The encoder comprises: a discrete cosine transform (DCT) module 51, a quantizer 53, an inverse transform module 61, an inverse quantizer 63, an intra prediction module 41, an inter prediction module 43, and a subtraction stage (−). The encoder also comprises a switch 47 and mode selection module 49. Each of the modules or blocks is preferably implemented as a portion of code stored on the transmitting terminal's storage medium 14 and arranged for execution on its processing apparatus 16, though the possibility of some or all of these being wholly or partially implemented in dedicated hardware circuitry is not excluded.

Each of the switch 47 and mode selection module 49 is arranged to receive an instance of input video stream comprising a plurality of macroblocks MB. The mode selection module 49 is arranged to select a coding mode “o” for each macroblock and is operatively coupled to the multiplexer 47 so as to control it to pass the output of the inverse quantizer 63 to the input of either the intra prediction module 41 or inter prediction module 43 as appropriate to the selected mode. The mode selection module 49 may also be arranged to indicate the selected mode “o” to the relevant prediction module 41, 43 (e.g. to indicate a 4×4 partition mode, 8×8 mode, skip mode, etc). The output the intra prediction module 41 or inter prediction module 43 is then coupled on to an input of the subtraction stage (−) which is arranged to receive the unencoded input video stream at its other input and subtract the predicted blocks from their unencoded counterparts, thus generating the residual signal. The residual blocks are then passed through the transform (DCT) module 51 where their residual values are converted into the frequency domain, then to the quantizer 53 where the transformed values are converted to discrete quantization indices. The quantized, transformed signal is fed back though the inverse quantizer 63 and inverse transform module 61 to generate a predicted version of the blocks (as would be seen at the decoder) for use by the selected prediction module 41, 43. An indication of the predictions used in the prediction modules 41,43, the motion vectors generated by the inter prediction module 43 and the quantized, transformed indices of the residual as generated by the transform and quantization modules 51, 53 are all output for inclusion in the encoded video stream, typically via a further, lossless encoding stage such as an entropy encoder (not shown) where the prediction values and transformed, quantized indices may be further compressed using lossless encoding techniques known in the art.

As mentioned, the present invention enables a hybrid RDO process which approaches the performance benefits of a full complexity loss adaptive RDO but with reduced computational complexity. Lower complexity in this context means incurring fewer processing cycles when executed on a processor. Particularly, the invention can be used to support an interaction between a simplified RDO and a loss-adaptive RDO process, thus helping to maximize the performance of the loss-adaptive RDO process when used in the context of a simplified rate-distortion expression.

A typical problem of today's real-time video encoding is that SSD computations in the macroblock mode selection are not feasible due to CPU constraints. A simplified rate-distortion-like cost function can alternatively be used (1a, 1b) but this does not provide the best performance in terms of the quality of the decoded video.

The present invention uses a simplified measure of source coding distortion, but adding a loss adaptive bias term D. In embodiments this may be expressed by the Lagrange function:

J _(hybrid) =D′(m,o)+D _(ep-ref)(m,o)+λ′R′(m,o)  (1c)

where:

$\begin{matrix} {D^{\prime} = {\sum\limits_{i}\; {{s_{i} - s_{{pred}_{i}}^{\prime}}}}} & \left( {1b} \right) \end{matrix}$

and D_(ep-ref) denotes the expected distortion within the reference block in the decoder due to error propagation, and is determined from the error propagation map D_(ep).

Another way to express the same optimization is:

$\begin{matrix} {{o_{opt} = {\underset{o}{argmin}\left( {{{SAD}\left( {{s(m)},{s_{pred}\left( {m,o} \right)}} \right)} + {D_{{ep} - {ref}}\left( {m,o} \right)} + {\lambda \; {R^{\prime}(o)}}} \right)}},} & \left( {1d} \right) \end{matrix}$

The lower-complexity measure of source coding distortion D′ is the simplified SAD between the original and predicted samples (without forward and inverse transform and quantization), and the lower complexity rate term R′ only measures the rate cost of the side information (not the residual).

D_(ep-ref) was derived based on a sum-of-squared-differences (SSD) distortion measure, so strictly shouldn't work as a term in a simplified RDO equation. However, the inventors have discovered that in fact this hybrid mixture gives a good performance approaching that of the full, loss adaptive RDO of Zhang et al. but with lower complexity.

Nonetheless, as a consequence the algorithm of Zhang et al. is not optimal when using the simplified rate-distortion distortion criterion. The simplified rate-distortion criterion above is based on heuristics and is very different from the full RDO using SSD. Therefore, simply using the algorithm of Zhang et al. is unlikely to yield optimal performance, i.e., adding the bias term D_(ep-ref)(m,o) using (3) in the previous section.

Therefore a particularly preferred embodiment of the present invention uses an adaptation of the algorithm in Zhang et al. for a simplified rate-distortion expression for macroblock mode selection. In particular, there is provided a data-driven procedure in order to maximize the end-to-end rate-distortion performance under lossy conditions.

In preferred embodiments, the computation of the error-propagation distortion map is adapted to use the simplified rate-distortion expression using a data-driven training procedure. This is likely to result in an improved rate-distortion performance. That is, e.g., parts of (3) are modified such that the computed D_(ep-ref)(m,o) is a better fit to the simplified rate-distortion expression.

The idea behind this preferred embodiment is related to the computation of the expected error propagation distortion map introduced above. In a preferred embodiment it makes the following change to the expression above for the update of the error propagated distortion map:

D _(ep)(m(k),n+1)=(1−p)D _(ep-ref)(m(k),n,o _(opt))+p(f(ŝ _(n)(o _(opt)),ŝ _(n−1))+D _(ec-ep)(m(k),n))  (4)

where f(ŝ_(n),ŝ_(n−1)) denotes a function of the reconstructed pixels of the current and previous encoded block m(k) representing the error-concealment reconstruction distortion which drives the error propagation recursion. D_(ep-ref)(m(k),n,o_(opt)) is computed from the previous error propagation distortion map and the current mode and motion vectors as was briefly described above. D_(ec-ep) can be estimated from the error propagation distortion map of the frame used for error concealment as briefly described above. In one example embodiment the form of the function is selected to be:

f(ŝ _(n) ,ŝ _(n−1))=(SAD(ŝ _(n) ,ŝ _(n−1)))^(γ(p,R)),  (4a)

or as an alternative:

f(ŝ _(n) ,ŝ _(n−1))=γ(p,R)·SSD(ŝ _(n) ,ŝ _(n−1))  (4b)

where the parameter γ(p,R) can for instance be trained so as to maximize the peak signal-to-noise ratio (PSNR) at the decoder for a relevant data set and transmission properties such as channel packet loss p and/or coding rate R. The parameter may also be dependent on round trip time (RTT) for a packet travelling over the channel from the transmitter to the receiver and back again (if the RTT is large the need for a loss-adaptive scheme is likely to be greater than when the RTT is small, assuming a recovery frame is obtained when loss is experienced). I.e. so the function γ in the above formulae would become γ(p, R, RTT), or indeed could, be a function of other combinations of such parameters.

That is to say, the algorithms (4) and (4a) are applied “offline” to a sample video stream, at the design stage, and a number of different values of γ are trialed and their results compared so as to maximize PSNR or other such transmission property acting as a training criterion. In the actual deployment the parameter γ(p,R) can then be read from the pre-trained table. By training the parameter γ(p,R) then D_(ep) is adapted, and thereby so is D_(ep-ref)(m,o), such as to better balance the source coding distortion versus the error propagated reference distortion in the simplified rate-distortion criterion of equation (1d). This is likely to result in an improved rate-distortion performance at the decoder under lossy conditions compared to Zhang's algorithm [2] in conjunction with the simplified rate-distortion criterion.

In embodiments, different values of γ(p,R) or γ(p, R, RTT) may be determined in advance for different training scenarios p, R and/or RTT and then applied to the actual outgoing video stream in dependence on a detected transmission scenario corresponding top, R and/or RTT. In the case that different values of γ are available, in some embodiments the selection of the current value may be adapted dynamically (i.e. on the fly, in real-time) based on detected current scenario (e.g. based on detected loss rate, coding rate and/or RTT).

In another embodiment one the factors (1−p) and p in equations (3) and (4) may be replaced by two trained parameters α(p,R) and β(p,R), both optimized to maximize e.g. the PSNR (peak signal-to-noise ratio) under certain predetermined conditions.

An alternative or complement to adapt the expression for the error propagation to fit the simplified rate-distortion expression is to estimate, by means of a rate-distortion model for the residual quantization, bias terms to the distortion and rate terms in equation (1a) or (1d) such to minimize the mismatch between the simplified rate-distortion expression and the original one of equation (2).

The invention may be implemented in an encoder is similar to that described in relation to FIG. 3, but with a modified mode selection module 49. It may be used to encode a video stream of the kind illustrated FIG. 1, and implemented in a communication system such as that of FIG. 2.

The working behind equations (4), (4a) and (4b) is now explained in more detail.

As mentioned, mode selection may involve optimizing (e.g. minimizing) a Lagrangian type function:

J=D(m,o)+λR(m,o),  (1)

where J represents the Lagrange function, D represents a measure of distortion (a function of mode o and macroblock m or macroblock sub-partition), R is the bitrate, and λ is a parameter defining a trade-off between distortion and rate.

In a conventional case the distortion term D only takes into account the source coding distortion, i.e. due to imperfections in the encoder such as the distortion introduced by quantization. It does not take into account the distortion that may be introduced due to loss of data over the channel, e.g. due to packet loss in transmission over a packet-based network 32.

On the other hand, loss adaptive techniques such as those of the present invention and Zhang [2] attempt to define a measure of “end-to-end” distortion taking into account both the source encoding and the distortion due to loss of data over the channel. The end-to-end distortion for a given (target) block may be described as:

D=(1−p)D _(arrival) +pD _(loss)  (5)

Where D_(arrival) is an estimate of the distortion that will be experienced if the target block does arrive at the decoder, and D_(loss) is an estimate of the distortion that will be experienced if the target block does not arrive at the decoder due to packet loss over the channel, e.g. due to loss of a packet comprising that block over a packet-based network 32. The parameter p is an estimate of the probability of a loss event occurring over the channel that results in the block in question being lost, e.g. an estimate of the probability of a packet loss.

D_(arrival) represents not only the source coding distortion but also the distortion that will be introduced due to distortion of a block's past, i.e. distortion in one or more reference blocks from which the target block is to be predicted. Therefore D_(arrival) comprises both a source coding distortion term D_(s) and an error propagation distortion term D_(ef-ref) which represents a distortion in the predicted target block's history (i.e. distortion in the target blocks' reference block which will carry forward into the target block):

D _(arrival) =D _(s) +D _(ep-ref)  (6)

D_(loss) comprises a loss due to concealment. If a target block is not received then the decoder will apply a concealment algorithm which could involve freezing a previously decoded block, or interpolating or extrapolating from one or more successfully decoded blocks (either from the current frame and/or a previous frame). Therefore D_(loss) can be identified as the distortion due to this concealment process:

D _(loss) =D _(ec)  (7)

So examining equation (5), the term D_(s) represents an estimate of the distortion that will be experienced if there is no loss at all, the term D_(ec) represents an estimate of the an estimate of the distortion that will be experienced if the target block is lost, and the term D_(ep-ref) represents an estimate of the distortion that will be experienced if the target block is successfully received but something in its history is lost (if the target block's reference block is lost, or the reference block's reference block is lost, etc.)

D_(s) and D_(ep-ref) are functions of encoding mode selection o. D_(ec) is not a function of mode selection o and so is dropped from the Lagrange expression (it does not matter how a lost block was encoded—it is still lost). Hence the optimization can be written as:

$\begin{matrix} {{o_{opt} = {\underset{o}{argmin}\left( {{D_{s}\left( {m,o} \right)} + {D_{{ep} - {ref}}\left( {m,o} \right)} + {\lambda \; {R\left( {m,o} \right)}}} \right)}},} & (2) \end{matrix}$

D_(s) is deterministic as it is based on information that can be known at the encoder, for example based on the difference between the raw input samples values s and the reconstructed sample values ŝ. The encoder runs a parallel instance of the decoder at the encoder side (or an approximation of it)—see the inset detailing the inter prediction module 43 in FIG. 3. The inter prediction module 43 comprises a motion compensation prediction (MCP) block 44 and addition stage (+) arranged to determine the reconstructed samples ŝ by combining the predicted samples ŝ_(pred) and the reconstructed residual {circumflex over (r)}, i.e. ŝ_(i)={circumflex over (r)}_(i)+ŝ_(pred) for each sample index i. In the case of inter encoding, at the encoder the predicted samples ŝ_(pred) may be the same as the samples of the reference block ŝ_(ref) (the reference block in the reference frame just being offset by the motion vector relative to the target frame—see FIG. 1 c, to be discussed again shortly).

Hence the encoder can determine the difference between the actual samples s and the reconstructed samples ŝ as seen at the encoder and (this so far ignores the possibility of loss which will introduce further distortion experienced at the decoder). The difference in samples may be calculated for example as the sum square difference (SSD) error over all sample indices i of the target block in question:

$\begin{matrix} {D_{s} = {\sum\limits_{i}\; \left\lbrack \left( {s_{i} - {\hat{s}}_{i}} \right)^{2} \right\rbrack}} & (8) \end{matrix}$

However, D_(ep-ref) remains to be estimated, which will be based on making some estimation concerning the channel over which the encoded data is to be transmitted (e.g. over packet-based network 32).

To achieve this, the mode selection module 49 in the encoder may be configured to maintain an error propagation distortion map D_(ep) describing the distortion of each macroblock or partition of a macroblock within the most recently encoded frame. The mode selection module 49 is also arranged to determine a probability p that the packet containing the reference block from which a target block is to be predicted will be lost over the channel (and therefore also to implicitly or explicitly determine a probability 1−p that the packet does arrive). In a preferred embodiment the probability p is predetermined at the design stage based on statistical modeling, in which case the mode selection module 49 determines p by retrieving a value from memory 14. However, another possibility would be that the mode selection module 49 determines p based on feedback from the receiver 22.

The error propagation map may be expressed as:

D _(ep)=(1−p)D _(ep-arrival) +pD _(loss)  (9)

The error propagation map D_(ep) comprises a distortion estimate for macroblock m or more preferably for each sub partition (block) m(k) within the most recently encoded frame. Hence it may be more explicitly written as:

D _(ep)(m(k))=(1−p)D _(ep-arrival)(m(k))+pD _(loss)(m(k))  (10)

where m(k) denotes the k^(th) sub-partition (e.g. block) of macroblock m and p the probability of packet loss.

D_(loss) is equal to D_(ec) as discussed above. D_(ep-arrival) represents the differences over the channel, i.e. the difference between the reconstructed samples at the encoder and the reconstructed at the decoder. For example this could be quantified in terms of the sum of squared differences (SSD):

$\begin{matrix} {D_{{ep} - {arrived}} = {\sum\limits_{i}\; \left( {{\hat{s}}_{i} - {\overset{\sim}{s}}_{i}} \right)^{2}}} & (11) \end{matrix}$

Where {tilde over (s)}_(i) are the samples (of indices i) received at the decoder taking into account both the source coding distortion and the distortion due to the channel. I.e. s_(i) are the raw unencoded input samples, ŝ_(i) are the reconstructed samples at the encoder taking into account the source coding distortion (e.g. due to quantization), and {tilde over (s)}_(i) are the samples taking into account the total end-to-end distortion including the lossy effect of the channel; s_(i)→ŝ_(i)→{tilde over (s)}_(i).

D_(ep-arrival) can be expanded to:

$\begin{matrix} {D_{{ep} - {arrived}} = {\sum\limits_{i}\; \left( {\left( {{\hat{s}}_{ref} - {\hat{r}}_{i}} \right) - \left( {{\overset{\sim}{s}}_{ref} - {\hat{r}}_{i}} \right)} \right)^{2}}} & (12) \end{matrix}$

where {circumflex over (r)}_(i) are the samples of the reconstructed residual. Therefore:

$\begin{matrix} {D_{{ep} - {arrived}} = {{\sum\limits_{i}\; \left( {{\hat{s}}_{ref} - {\overset{\sim}{s}}_{ref}} \right)^{2}} = D_{{ep} - {ref}}}} & (13) \end{matrix}$

So substituting into equations (9) and (1), the error propagation map can be rewritten as:

D _(ep)=(1−p)D _(ep-ref) +pD _(ec)  (14)

or:

D _(ep)(m(k))=(1−p))D _(ep-ref)(m(k))+pD _(ec)(m(k))  (15)

Considering the mode optimization problem, it may also be written:

D _(ep)(m(k),n+1)=(1−p)D _(ep-ref)(m(k),n,o _(opt))+pD _(ec)(m(k),n,o _(opt))  (16)

where n is the frame number, i.e. D_(ep)(n+1) is the error propagation map to be used for making the mode selection for frame number n+1 given the existing decision o_(opt) and distortion D_(ep)(n) map for frame n.

As in Zhang et al., the D_(ec) term may be also expanded:

D _(ep)(m(k),n+1)=(1−p)D _(ep-ref)(m(k),n,o _(opt))+p(D _(ec-rec)(m(k),n,o _(opt))+D _(ec-ep)(m(k),n)),  (3)

where D_(ec-rec) denotes the SSD between the reconstructed and error concealed pixels in the encoder, and D_(ec-ep) the expected SSD between the error concealed pixels in the encoder and decoder.

Examining equation (3), as explained above, the term D_(ep-ref) represents the distortion that will be experienced if the target block is successfully received but something in its history is lost (if the target block's reference block is lost, or the reference block's reference block is lost, etc.). Further, D_(ec-rec) represents an estimate of the distortion due to the nature of the concealment algorithm itself (somewhat analogous to the intrinsic source coding distortion D_(s) for prediction). D_(ec-ep) then represents an estimate of the distortion that will be experienced if both the target block is lost (and so needs to be concealed at the decoder) and something in the concealed target block's history is also lost (if the block from which concealment is done is lost, or the block from which that block is predicted or concealed is lost, etc.). That is, D_(ec-ep) represents the distortion introduced in the error concealed block because of a corrupt reference for concealment (due to prior losses), i.e. an encoder-decoder reference mismatch.

So the distortion map D_(ep) comprises a contribution due to new loss, resulting from D_(ec-rec) and in part from D_(ec-ep); and a contribution due to past loss, resulting from D_(ep-ref) and in part also from D_(ec-ep).

For the first frame in a sequence the frame will be coded with intra coding, in which case D_(ep-ref)=0 and therefore D_(ep)=pD_(ec).

The error concealment distortion D_(ec) is calculated by the mode selection module 49. The term D_(ec-rec) is based on knowledge of the concealment algorithm, and may depend on the particular error concealment algorithm used. D_(ec-ep) is calculated based on the existing (most recent) distortion map in a manner analogous to D_(ep-ref), e.g. by copying the distortion of a co-located block in the case of a basic concealment algorithm or calculating a weighted sum of the distortions from multiple previously encoded blocks b1-b4 if a more sophisticated concealment is used that attempts to extrapolate motion (by analogy see discussion in relation to FIG. 1 c below). Other ways of calculating D_(ec) could be used—this could be any estimation of a difference between the reconstructed samples in the encoder and the error concealed samples as would be seen ay the decoder (i.e. the samples copied, interpolated or extrapolated from a previous received frame or a received region of the same frame to conceal the lost frame or region).

The mode selection module 49 then maintains the error propagation map for each subsequent inter predicted frame by updating it following each mode selection decision, now including a calculation of D_(ep-ref) from knowledge of the existing error map using the motion vectors for the frame in question.

An example of inter prediction (motion estimation) is illustrated in FIG. 1 c. Four example blocks b1, b2, b3 and b4 are shown in a reference frame F_(t) (number n), the reference frame having already been encoded. The blocks of the target frame F_(t+1) (number n+1) are to be predicted from the reference frame F_(t). For example consider a target block b₁′ in the target frame F_(t+1). To this end the motion prediction module 44 determines a motion vector defining an offset between the target block in the target frame F_(t+1) and a reference block (shown by the dotted line) in the reference frame F_(t), such that when the reference block is translated from the offset position in the reference frame F_(t) into the position of the target block b₁′ in the target frame F_(t+1) it provides a best estimate of the target block b₁′. Note therefore that the dotted reference block is not necessarily an indexable block in the reference frame F_(t), i.e. is not necessarily a predetermined subdivision of the reference frame, and may be offset by any arbitrary amount (and in fact may even be offset by a fractional number of pixels). Hence the reference block is made up of a contribution from four actual indexable blocks b1, b2, b3 and b4.

Accordingly, the calculation performed by the mode selection module 49 to determine D_(ep-ref) for use in the update of the error propagation map D_(ep)(n+1) comprises calculating a weighted sum of the distortions recorded for blocks b1 to b4 in the existing map D_(ep)(n):

$\begin{matrix} {D_{{ep} - {ref}} = {\sum\limits_{i = 1}^{4}\; {w_{i}{D_{ep}(i)}}}} & (17) \end{matrix}$

where w_(i) is the weight representing the contribution from block b_(i) and D_(ep)(i) is the error propagation map entry for block b_(i).

The above describes a process of determining an initial error propagation map D_(ep), using the error propagation map to select an optimal coding mode decision o_(opt) for a subsequent coding, using the coding decision to update the map D_(ep), then using the updated map in the next coding decision, and so forth, wherein the error propagation map represents an end-to-end distortion including an estimated effect of loss over the channel. E.g. reference is made again to Zhang [2]. This may be referred to herein as loss-adaptive rate-distortion optimization (LARDO).

However, in a preferred embodiment of the present invention the error propagation map is modified as follows:

D _(ep)(m(k),n+1)=(1−p)D _(ep-ref)(m(k),n,o _(opt))+p(f(ŝ _(n)(o _(opt)),ŝ _(n−1))+D _(ec-ep)(m(k),n)),  (4)

Where f is a function based on the sum of absolute differences between the reconstructed samples of the block of the current frame in question and the reconstructed samples of the block of a previously encoded frame which would be used to conceal the lost samples. For example:

f(ŝ _(n) ,ŝ _(n−1))=(SAD(ŝ _(n) ,ŝ _(n−1)))^(γ(p,R))  (4a)

Hence the term D_(ec-rec) that represents the intrinsic distortion of the concealment algorithm is modified to be based on an SAD type measure instead of SSD. Preferably this is the SAD raised to the power of a trained parameter γ.

Another option would be:

f(ŝ _(n) ,ŝ _(n−1))=γ(p,R)·SSD(ŝ _(n) ,ŝ _(n−1))  (4b)

A mentioned, the factors (1−p) and p in equation (1d) may also be replaced by two trained parameters α(p,R) and β(p,R) respectively.

In either of the above examples, γ could also be a function of round trip time RTT, i.e. γ(p, R, RTT), or of other combinations of such parameters.

A further modification that may optionally be used in conjunction with the present invention is now described.

A problem with the algorithm in Zhang et al. is that it does not necessarily take into account the impact of a potential distortion into the future in an optimal way. From a perceptual point of view the duration of an error is an important factor, and the inventors believe that even a small but persistent potential expected error propagation distortion should eventually trigger a selection of intra coding mode. In the following described embodiments it is exemplified how to include temporal integration into the expression of expected error-propagation distortion in order to facilitate this.

Using the algorithm by Zhang et al., the expected potential error-propagation distortion from (3) is not always high enough to trigger intra coding and as a result artifacts will remain present until next intra frame or scene change. Instead, if the potential error-propagation is integrated over time it is likely that an intra coding will be triggered and the remaining artifacts vanish over time. An example embodiment of how this can be implemented is presented in the following.

As mentioned, there is a problem with existing loss-adaptive RDO techniques in that they do not take into account the impact of past loss accumulating forward into the future, particularly in circumstances where there is little or no motion such as a static background or approximately static background. In such circumstances the inventors have noted that:

D _(ec-rec)≈0  (18)

In a basic concealment algorithm this is because the concealed block is copied from a preceding co-located block, and in the case of a static background the preceding co-located block will be the same as the current concealed block. That is, the error concealed and reconstructed samples in the encoder will be the same; or put another way, the concealment algorithm itself does not intrinsically introduce any distortion. A similar effect will also occur in a more sophisticated concealment algorithm.

Furthermore:

D _(ec-ep) ≈D _(ep)  (19)

This is because, in absence of any intrinsic distortion from the concealment, the difference between the error concealed samples at the encoder and those as estimated to be seen at the decoder will only be copied from the existing error propagation map.

Substituting (18) and (19) into equation (3), it can be seen that this means:

D _(ep)(n+1)≈(1−p)D _(ep)(n)+pD _(ep)(n)≈D _(ep)(n)  (20)

That is, in circumstances where the contribution from new loss is zero or negligible, the updated propagation map reduces to a contribution only from past loss (loss in the history used for prediction and/or concealment). Looked at another way, in the case of little or no motion, e.g. a substantially static background, the effect of any further loss over a channel and the associated concealment at the decoder will in itself have no intrinsic effect on the distortion, because a block copied or extrapolated from one frame to the next should in principle be identical (or in the case of a spatial concealment algorithm, a block which is copied, extrapolated or interpolated from one or more nearby blocks of a large, static region of uniform background will be very similar). The result is that D_(ep) will remain the same indefinitely and not grow over time.

However, in reality the distortion will become increasingly relevant from a perceptual point of view, because the duration of an error is important in the perception of the error. That is to say, it is not just the magnitude of distortion that is relevant from a perceptual point of view, but also its duration.

A problem therefore exist in that, using existing techniques such Zhang et al., the distortion map which forms a basis for the making coding mode decisions will not always trigger intra coding early enough to prevent perceptually relevant artifacts.

In Zhang et al. the error propagation map may increase over time, but only due to a contribution to the distortion that arises from continued ongoing loss over the channel, i.e. only due to new loss and associated concealment.

To address this problem, the present invention proscribes the use of an error propagation map which, in circumstances such as a static background where the contribution from new loss is zero or negligible, reduces to an expression which accumulates the contribution from past loss into the future:

D _(ep)(n+1)=εD _(ep)(n)  (21)

where ε>1. This may be considered as a temporal integration of the contribution from past loss over time.

For example, modifying Zhang et al. the formula for the distortion map would become:

D _(ep)(m(k),n+1)=ε(1−p)D _(ep-ref)(m(k),n,o _(opt))+p(D _(ec-rec)(m(k),n,o _(opt))+D _(ec-ep)(m(k),n)),  (22)

where ε>1. As mentioned, a sufficiently large factor ε and a nonzero D_(ep-ref) will result in D_(ep) growing even in conditions where the error concealment reconstruction distortion D_(ec-rec) is zero, and thus, eventually triggering an intra coding.

That is, the effect of historical loss is amplified increasingly as more time passes, giving this distortion a greater weight in the optimization problem. Even if the actual distortion estimated in terms of difference between samples is not necessarily growing, the perception of the distortion becomes more significant with time and so older distortion should be given a greater weight when choosing whether to use inter or intra coding to encode the next frame or region.

Equation (22) can optionally be combined with the other teachings above so that D_(ec-rec) becomes the SAD-based measure of equation (4a) or the SSD-based measure of equation (4b).

It will be appreciated that the above embodiments have been described only by way of example.

For instance, the invention can be extended to other ways of tuning the loss-adaptive optimization process besides tuning D_(ec-rec). An alternative example would be to leave D_(ec-rec) unmodified (e.g. as in Zhang) and instead apply a function to D_(ep-ref), such that D_(ep-ref) becomes a function of a trained parameter γ, e.g. D_(ep-ref) raised to the power of γ or D_(ep-ref) multiplied by γ. In this case the error propagation map could be expressed as:

D _(ep)=(1−p)f(D _(ep-ref))+p(D _(ec-rec) +D _(ec-ep))

where f(D_(ep-ref)) could be for example D_(ep-ref) ^(γ(p,R)) or D_(ep-ref) ^(γ(p,R,RTT)) or γ(p,R)·D_(ep-ref) or γ(p, R, RTT)·D_(ep-ref).

Further, the parameters λ,α, β and ε in the various formulae above may be tuned by the system designer. There is no right or wrong value for these parameters—the preferred values will depend on the particular quality the system designer decides to tolerate and the bitrate that can be supported by the channel. By way of example, in one embodiment e may be in the range 1.03 to 1.05. A particular value of λ is suggested by H.264 though this may also be tuned according to system design.

In a particularly beneficial embodiment, the mode selection module 49 may be configured to use different values of λ, α, β and/or ε for different bitrates and/or channel conditions (e.g. packet loss and round-trip time). In this case the values may be adapted dynamically based on the currently detected channel condition(s), e.g. as reported in feedback from the decoder; and/or based on a dynamic setting or change of bitrate, e.g. based on a requested bitrate from the decoder or based on a user setting.

Note again that where a contribution due to loss is mentioned in this application, or anything stating what happens “if” data lost over the channel or such like, this only relates to a probabilistic assumption (e.g. p) made by the encoder about what might be experienced by the decoder—the encoder of course does not know what will happen. The probabilistic assumption may be predetermined at the design stage based on statistical network modeling, and/or could even be determined dynamically based on feedback from the decoder.

While the above has been described in terms of slices, macroblocks and blocks, these terms are not intended to be limiting and the ideas described herein are not limited to any particular way of dividing or subdividing a claim. Further, the distortion map may cover a whole frame or a region within a frame, and coding decision process may be applied over the whole frame or only for a region within a frame.

Further, it is possible to use other processes that use other combinations of the simplifying approximations discussed above. For example, it is possible for the lower-complexity SAD based measures to exclude only transform but not quantization (i.e. to quantize in the spatial domain). Another example is to exclude quantization and use transformation in real-time encoding (e.g. using the sum of absolute transformed differences as a distortion measure, which applies a frequency transform to the differences between the pixels in the original block and the reference block, which improves coding performance at a slight extra processing cost over basic SAD). Another example is to include all of prediction, transform and quantization but still use a sum of absolute differences (SAD) as the distortion measure instead of sum of squared differences (SSD). In other examples, the higher-complexity SSD based measures could take into account the effect of loss over a channel or error propagation, but still exclude transform and/or quantization, and/or still be based on an SAD or other distortion measure. Further, different combinations of SAD and/or SSD could be used for the bias term. Generally, all combinations are possible.

Further, where the present invention is described in terms of two frames n and n+1, according to certain embodiments of the invention it is not necessary for these to refer to two adjacent frames (though that may be the case in existing codecs). In some embodiments it is possible that inter prediction could be performed relative to an even earlier frame, and as such n and n+1 may be used in relation to the present invention to refer respectively to any previously encoded frame or image portion and a subsequent frame or portion to be predicted from it.

It should be understood that the block, flow, and network diagrams may include more or fewer elements, be arranged differently, or be represented differently. It should be understood that implementation may dictate the block, flow, and network diagrams and the number of block, flow, and network diagrams illustrating the execution of embodiments of the invention.

It should be understood that elements of the block, flow, and network diagrams described above may be implemented in software, hardware, or firmware. In addition, the elements of the block, flow, and network diagrams described above may be combined or divided in any manner in software, hardware, or firmware. If implemented in software, the software may be written in any language that can support the embodiments disclosed herein. The software may be stored on any form of non-transitory computer readable medium, such as random access memory (RAM), read only memory (ROM), compact disk read only memory (CD-ROM), flash memory, hard drive, and so forth. In operation, a general purpose or application specific processor loads and executes the software in a manner well understood in the art.

Other variants may become apparent to a person skilled in the art given the disclosure herein. The scope of the invention is not limited by the described embodiments but only by the appendant claims. 

1. A method of encoding an outgoing video stream comprising a plurality of frames, each frame comprising a plurality of image portions, the method comprising: for each target image portion to be encoded, selecting a preferred one of a set of encoding modes by optimizing a function comprising an estimate of distortion and a measure of bit rate required to encode the target image portion; encoding the target image portion into the outgoing video stream using the selected encoding mode; and transmitting the encoded outgoing video stream over a lossy channel; wherein the estimate of distortion comprises a first term representing source coding distortion, and a bias term representing an estimate of distortion that would be experienced due to loss over said channel; and wherein the bias term is determined based on a trained parameter trained based on a sample video stream.
 2. The method of claim 1, wherein: the bias term is based on a second term representing an estimate of the distortion that would be experienced, if the target portion does arrive over the channel, due to non arrival of a reference portion in the target portion's history from which prediction of the target portion depends, and on a concealment term representing an estimate of distortion that would be experienced due to concealment; the concealment term comprises a third term representing a measure of concealment distortion of the target portion relative to an image portion that would be used to conceal loss of the target portion if the target portion is lost over the channel, and a fourth term representing an estimate of distortion that would be experienced due to loss of an image portion in the target portion's history upon which concealment of the target portion depends; and one of the third term and the second term comprises said trained parameter.
 3. The method of claim 2, wherein the third term comprises said trained parameter.
 4. The method of claim 1, wherein the first term is based on a lower complexity measure of difference between samples than one or more terms upon which the bias term is based.
 5. The method of claim 1, wherein the first term is based on a sum of absolute differences between original samples and predicted samples of the target image portion.
 6. The method of claim 3, wherein the third term is based on a sum of absolute differences between reconstructed samples of the target image portion and reconstructed samples of the image portion that would be used to conceal loss of the target portion.
 7. The method of claim 6, wherein the third term comprises the sum of absolute differences raised to the power of the trained parameter.
 8. The method of claim 3, wherein the third term comprises the sum of squared differences multiplied by the trained parameter.
 9. The method of claim 1, wherein the trained parameter is trained to maximize a ratio of signal power to noise.
 10. The method of claim 1, wherein the trained parameter is a function of one or more of a loss probability, coding rate and round trip time.
 11. The method of claim 2, wherein one or both of the second and fourth terms are based on a sum of squared differences between samples.
 12. The method of claim 3, comprising determining a probability p that a packet will be lost over the channel, wherein the second term is weighted by a factor of 1−p, and the concealment term is weighted by a factor of p.
 13. The method of claim 3, comprising determining a probability p that a packet will be lost over the channel, wherein the second term is weighted by a trained factor α(p,R) being a function of p and the bit rate R, and the concealment term is weighted by a trained factor β(p,R) also being a function of p and the bit rate R.
 14. The method of claim 2, wherein the bias term is based on an entry in an error propagation distortion map comprising said second term and concealment term, and the method comprises: updating the error propagation distortion map after each encoding mode selection, and determining the error propagation bias term from the error propagation distortion map for use in each respective subsequent encoding mode selection.
 15. The method of claim 1, wherein the selected encoding mode o_(opt) is calculated by: $o_{opt} = {\underset{o}{argmin}\left( {{{D^{\prime}\left( {m,o} \right)} + {D_{{ep} - {ref}}\left( {m,o} \right)} + {\lambda \; {R^{\prime}\left( {m,o} \right)}}},} \right.}$ where D′(m,o)+D_(ep-ref)(m,o)+λR′(m,o) is said function, D′ is the first term, D_(ep-ref)(m,o) is the bias term, R′(m,o) is the bitrate, λ is a factor representing a trade-off between distortion and bitrate, and m is an index of the target image portion.
 16. The method of claim 3, comprising determining a probability p that a packet will be lost over the channel, wherein the updated distortion map D_(ep) for a frame n+1 is calculated according to: D _(ep)(m(k),n+1)=(1−p)D _(ep-ref)(m(k),n,o _(opt))+p(f(ŝ _(n)(o _(opt)),ŝ _(n−1))+D _(ec-ep)(m(k),n)) where n represents a previously encoded frame, m(k) is the k^(th) partition of the m^(th) image portion, D_(ep-ref) is the second term, f(ŝ_(n),ŝ_(n−1)) is the third term, and D_(ec-ep) is the fourth.
 17. The method of claim 7, wherein: f(ŝ _(n) ,ŝ _(n−1))=(SAD(ŝ _(n) ,ŝ _(n−1)))^(γ) where SAD is the sum of absolute differences and γ is the trained parameter.
 18. The method of claim 8, wherein: f(ŝ _(n) ,ŝ _(n−1))=γ·SSD(ŝ _(n) ,ŝ _(n−1)) where SSD is the sum of squared differences and γ(p,R) is the trained parameter.
 19. The method of claim 1, wherein the encoding modes comprise at least (i) an intra frame mode which encodes the target image portion relative to a reference image portion in the same frame, and (ii) an inter frame encoding mode which encodes the target image portion relative to a reference image portion in a previously encoded frame
 20. The method of claim 1, wherein the set of encoding modes comprises a plurality of intra frame modes.
 21. The method of claim 1, wherein the set of encoding modes comprises a skip mode.
 22. The method of claim 1, wherein the first term takes into account distortion due to prediction coding but not distortion due to quantization.
 23. The method of claim 3, wherein the third term takes into account distortion due to prediction coding but not distortion due to quantization.
 24. The method of claim 3, wherein one or both of the second and fourth terms take into account distortion due to both prediction coding and quantization.
 25. The method of claim 22, wherein the first term takes into account distortion due to prediction coding but not distortion due to transformation from a spatial domain representation to a transform domain representation.
 26. The method of claim 3, wherein the third term takes into account distortion due to prediction coding but not distortion due to quantization.
 27. The method of claim 25, wherein one or both of the second and fourth terms take into account distortion due to both prediction coding and transformation from a spatial domain representation to a transform domain representation.
 28. The method of claim 1, wherein the bit rate does not take into account a cost of encoding a residual signal.
 29. The method of claim 1, wherein the bias term integrates an effect of past loss forward over time.
 30. The method of claim 2, wherein the second term comprises said trained parameter, the method comprises determining a probability p that a packet will be lost over the channel, wherein the updated distortion map D_(ep) for a frame n+1 is calculated according to: D _(ep)(m(k),n+1)=(1−p)f(D _(ep-ref)(m(k),n,o _(opt)))+p(D _(ec-rec)(m(k),n,o _(opt))+D _(ec-ep)(m(k),n)) where f(D_(ep-ref))=D_(ep-ref) ^(γ) or γ·D_(ep-ref) and γ is the trained parameter.
 31. A computer program product for encoding an outgoing video stream comprising a plurality of frames, each frame comprising a plurality of image portions, the computer program product being embodied on a non-transitory computer-readable medium and configured so as when executed on a processor to perform the operations of: for each target image portion to be encoded, selecting a preferred one of a set of encoding modes by optimizing a function comprising an estimate of distortion and a measure of bit rate required to encode the target image portion; encoding the target image portion into the outgoing video stream using the selected encoding mode; and transmitting the encoded outgoing video stream over a lossy channel; wherein the estimate of distortion comprises a first term representing source coding distortion, and a bias term representing an estimate of distortion that would be experienced due to loss over said channel; and wherein the bias term is determined based on a trained parameter trained based on a sample video stream.
 32. The computer program product of claim 31, wherein: the bias term is based on a second term representing an estimate of the distortion that would be experienced, if the target portion does arrive over the channel, due to non arrival of a reference portion in the target portion's history from which prediction of the target portion depends, and on a concealment term representing an estimate of distortion that would be experienced due to concealment; the concealment term comprises a third term representing a measure of concealment distortion of the target portion relative to an image portion that would be used to conceal loss of the target portion if the target portion is lost over the channel, and a fourth term representing an estimate of distortion that would be experienced due to loss of an image portion in the target portion's history upon which concealment of the target portion depends; and one of the third term and the second term comprises said trained parameter.
 33. The computer program product of claim 32, wherein the third term comprises said trained parameter.
 34. The computer program product of claim 31, wherein the first term is based on a lower complexity measure of difference between samples than one or more terms upon which the bias term is based.
 35. The computer program product of claim 31, wherein the first term is based on a sum of absolute differences between original samples and predicted samples of the target image portion.
 36. The computer program product of claim 33, wherein the third term is based on a sum of absolute differences between reconstructed samples of the target image portion and reconstructed samples of the image portion that would be used to conceal loss of the target portion.
 37. The computer program product of claim 31, wherein the trained parameter is trained to maximize a ratio of signal power to noise.
 38. The computer program product of claim 31, wherein the trained parameter is a function of one or more of a loss probability, coding rate and round trip time.
 39. The computer program product of claim 32, wherein one or both of the second and fourth terms are based on a sum of squared differences between samples.
 40. The computer program product of claim 33, wherein the code is configured to determine a probability p that a packet will be lost over the channel, wherein the second term is weighted by a trained factor α(p,R) being a function of p and the bit rate R, and the concealment term is weighted by a trained factor β(p,R) also being a function of p and the bit rate R.
 41. An apparatus for encoding an outgoing video stream comprising a plurality of frames, each frame comprising a plurality of image portions, the apparatus comprising: an encoder configured, for each target image portion to be encoded, to select a preferred one of a set of encoding modes by optimizing a function comprising an estimate of distortion and a measure of bit rate required to encode the target image portion; wherein the encoder is configured to encode the target image portion into the outgoing video stream using the selected encoding mode; the apparatus comprises a transmitter for transmitting the encoded outgoing video stream over a lossy channel; the estimate of distortion comprises a first term representing source coding distortion, and a bias term representing an estimate of distortion that would be experienced due to loss over said channel; and the encoder is configured such that the bias term is determined based on a trained parameter trained based on a sample video stream.
 42. The apparatus of claim 41, wherein: the bias term is based on a second term representing an estimate of the distortion that would be experienced, if the target portion does arrive over the channel, due to non arrival of a reference portion in the target portion's history from which prediction of the target portion depends, and on a concealment term representing an estimate of distortion that would be experienced due to concealment; the concealment term comprises a third term representing a measure of concealment distortion of the target portion relative to an image portion that would be used to conceal loss of the target portion if the target portion is lost over the channel, and a fourth term representing an estimate of distortion that would be experienced due to loss of an image portion in the target portion's history upon which concealment of the target portion depends; and one of the third term and the second term comprises said trained parameter.
 43. The apparatus of claim 42, wherein the third term comprises said trained parameter.
 44. The apparatus of claim 41, wherein the first term is based on a lower complexity measure of difference between samples than one or more terms upon which the bias term is based.
 45. The apparatus of claim 41, wherein the first term is based on a sum of absolute differences between original samples and predicted samples of the target image portion.
 46. The apparatus of claim 43, wherein the third term is based on a sum of absolute differences between reconstructed samples of the target image portion and reconstructed samples of the image portion that would be used to conceal loss of the target portion.
 47. The apparatus of claim 41, wherein the trained parameter is trained to maximize a ratio of signal power to noise.
 48. The apparatus of claim 41, wherein the trained parameter is a function of one or more of a loss probability, coding rate and round trip time.
 49. The apparatus of claim 42, wherein one or both of the second and fourth terms are based on a sum of squared differences between samples.
 50. The apparatus of claim 43, wherein the second term is weighted by a trained factor α(p,R) being a function of the bit rate R a probability p that a packet will be lost over the channel, and the concealment term is weighted by a trained factor β(p,R) also being a function of p and the bit rate R. 